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1、FoundationsofDataScience1JohnHopcroftRavindranKannanVersion21/8/2014Thesenotesarea rstdraftofabookbeingwrittenbyHopcroftandKannanandinmanyplacesareincomplete.However,thenotesareingoodenoughshapetopreparelecturesforamoderntheoreticalcourseincomputerscience.Pleasedonotputsolutionstoexercisesonl
2、ineasitisimportantforstudentstoworkoutsolutionsforthemselvesratherthancopythemfromtheinternet.ThanksJEH1Copyright2011.Allrightsreserved1Contents1Introduction72High-DimensionalSpace102.1PropertiesofHigh-DimensionalSpace.....................122.2TheLawofLargeNumbers..........................132
3、.3TheHigh-DimensionalSphere.........................152.3.1TheSphereandtheCubeinHighDimensions............162.3.2VolumeandSurfaceAreaoftheUnitSphere.............172.3.3TheVolumeisNeartheEquator...................202.3.4TheVolumeisinaNarrowAnnulus..................232.3.5TheSurfaceAreaisNearth
4、eEquator................242.4VolumesofOtherSolids............................262.5GeneratingPointsUniformlyatRandomontheSurfaceofaSphere....272.6GaussiansinHighDimension.........................272.7BoundsonTailProbability...........................332.8Applicationsofthetailbound.............
5、............352.9RandomProjectionandJohnson-LindenstraussTheorem..........382.10BibliographicNotes...............................412.11Exercises.....................................423Best-FitSubspacesandSingularValueDecomposition(SVD)523.1SingularVectors.................................533.2
6、SingularValueDecomposition(SVD).....................563.3BestRankkApproximations.........................583.4LeftSingularVectors..............................603.5PowerMethodforComputingtheSingularValueDecomposition......623.6ApplicationsofSingularValueDecomposition................643.6.1Pri
7、ncipalComponentAnalysis.....................643.6.2ClusteringaMixtureofSphericalGaussians.............653.6.3SpectralDecomposition........................713.6.4SingularVectorsandRankingDocuments..............713.6.5AnApplicationofSVDtoaDiscr
